Surreal backgrounds, asymmetry, and indecipherable text can help identify deepfakes
Differences in the backgrounds of training data with the same subject introduce variability, resulting in background textures and text that lack realistic detail. Difficulties with managing long-distance dependencies, the complexity of hair, and other real-world phenomena introduce errors that can be used to flag AI-generated content.




